{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1ce03885",
   "metadata": {},
   "source": [
    "运行环境：jupyter notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "22015923",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "\n",
    "from sklearn.model_selection import train_test_split # 切分数据\n",
    "from sklearn.metrics import mean_squared_error #评价指标\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "import seaborn as sns\n",
    "from sklearn import preprocessing\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "876c1f29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>distance_from_home</th>\n",
       "      <th>distance_from_last_transaction</th>\n",
       "      <th>ratio_to_median_purchase_price</th>\n",
       "      <th>repeat_retailer</th>\n",
       "      <th>used_chip</th>\n",
       "      <th>used_pin_number</th>\n",
       "      <th>online_order</th>\n",
       "      <th>fraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>57.877857</td>\n",
       "      <td>0.311140</td>\n",
       "      <td>1.945940</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.829943</td>\n",
       "      <td>0.175592</td>\n",
       "      <td>1.294219</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.091079</td>\n",
       "      <td>0.805153</td>\n",
       "      <td>0.427715</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.247564</td>\n",
       "      <td>5.600044</td>\n",
       "      <td>0.362663</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>44.190936</td>\n",
       "      <td>0.566486</td>\n",
       "      <td>2.222767</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.586408</td>\n",
       "      <td>13.261073</td>\n",
       "      <td>0.064768</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.724019</td>\n",
       "      <td>0.956838</td>\n",
       "      <td>0.278465</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.848247</td>\n",
       "      <td>0.320735</td>\n",
       "      <td>1.273050</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.876632</td>\n",
       "      <td>2.503609</td>\n",
       "      <td>1.516999</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8.839047</td>\n",
       "      <td>2.970512</td>\n",
       "      <td>2.361683</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   distance_from_home  distance_from_last_transaction  \\\n",
       "0           57.877857                        0.311140   \n",
       "1           10.829943                        0.175592   \n",
       "2            5.091079                        0.805153   \n",
       "3            2.247564                        5.600044   \n",
       "4           44.190936                        0.566486   \n",
       "5            5.586408                       13.261073   \n",
       "6            3.724019                        0.956838   \n",
       "7            4.848247                        0.320735   \n",
       "8            0.876632                        2.503609   \n",
       "9            8.839047                        2.970512   \n",
       "\n",
       "   ratio_to_median_purchase_price  repeat_retailer  used_chip  \\\n",
       "0                        1.945940              1.0        1.0   \n",
       "1                        1.294219              1.0        0.0   \n",
       "2                        0.427715              1.0        0.0   \n",
       "3                        0.362663              1.0        1.0   \n",
       "4                        2.222767              1.0        1.0   \n",
       "5                        0.064768              1.0        0.0   \n",
       "6                        0.278465              1.0        0.0   \n",
       "7                        1.273050              1.0        0.0   \n",
       "8                        1.516999              0.0        0.0   \n",
       "9                        2.361683              1.0        0.0   \n",
       "\n",
       "   used_pin_number  online_order  fraud  \n",
       "0              0.0           0.0    0.0  \n",
       "1              0.0           0.0    0.0  \n",
       "2              0.0           1.0    0.0  \n",
       "3              0.0           1.0    0.0  \n",
       "4              0.0           1.0    0.0  \n",
       "5              0.0           0.0    0.0  \n",
       "6              0.0           1.0    0.0  \n",
       "7              1.0           0.0    0.0  \n",
       "8              0.0           0.0    0.0  \n",
       "9              0.0           1.0    0.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv('card_transdata (1).csv')\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bf8be371",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "原数据行列值: (1000000, 8)\n",
      "标签值分布：\n",
      " 0.0    912597\n",
      "1.0     87403\n",
      "Name: fraud, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print('原数据行列值:',data.shape)\n",
    "print('标签值分布：\\n',data.fraud.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b6cc1b1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "标签值分布：\n",
      " 0.0    912597\n",
      "1.0     87403\n",
      "Name: fraud, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print('标签值分布：\\n',data.fraud.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ea3450da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'fraud'}, ylabel='fraud'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#画出分布的饼状图\n",
    "plt.figure(figsize = (7,7))\n",
    "explod=(0.3,0.3)\n",
    "colors='#A7D67F','#FFCC80'\n",
    "\n",
    "data['fraud'].value_counts().plot(\n",
    "            kind = 'pie',  #选择图形类型\n",
    "           autopct = '%.2f%%',  #饼图中添加数值标签\n",
    "            explode=explod,\n",
    "           radius = 1,  #设置饼图半径\n",
    "           colors=colors,\n",
    "           startangle = 180,  #设置饼图的初始角度\n",
    "           counterclock = False,  #将饼图的顺序设置为顺时针方向\n",
    "           title = 'fraud',  #为饼图添加标题\n",
    "#            shadow=True,       #阴影\n",
    "           textprops = {'fontsize':14, 'color':'black'},  #设置文本标签的属性值\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cfdf9c82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ba157331",
   "metadata": {},
   "outputs": [],
   "source": [
    "#把数据降到一万条\n",
    "# from sklearn.model_selection import train_test_split  #对数据按’fraud标签‘分层取样，确保取样后比列不变。\n",
    "\n",
    "# data_new, _ = train_test_split(data_pre, test_size=0.99, stratify=data_pre[['fraud']])\n",
    "# print ('新数据大小：',data_new.shape)\n",
    "# print('新数据标签分布：\\n', data_new.fraud.value_counts() ) #取样后比例未变。\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "be9d8a90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "distance_from_home                0\n",
       "distance_from_last_transaction    0\n",
       "ratio_to_median_purchase_price    0\n",
       "repeat_retailer                   0\n",
       "used_chip                         0\n",
       "used_pin_number                   0\n",
       "online_order                      0\n",
       "fraud                             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#缺失值检测\n",
    "\n",
    "#isnull()方法：在数据文件中，对于非缺失值返回False，对于缺失值返回Ture\n",
    "#isnull().any()方法：一个序列（一行）中有缺失值返回Ture，否则返回False\n",
    "#isnull().sum()方法：对每一列的缺失值进行求和，在这里我们选择这个方法去检测缺失值\n",
    "#如结果所示，该数据集没有缺失值\n",
    "\n",
    "#缺失值检查\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85dbde9a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2c33d051",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>distance_from_home</th>\n",
       "      <th>distance_from_last_transaction</th>\n",
       "      <th>ratio_to_median_purchase_price</th>\n",
       "      <th>repeat_retailer</th>\n",
       "      <th>used_chip</th>\n",
       "      <th>used_pin_number</th>\n",
       "      <th>online_order</th>\n",
       "      <th>fraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [distance_from_home, distance_from_last_transaction, ratio_to_median_purchase_price, repeat_retailer, used_chip, used_pin_number, online_order, fraud]\n",
       "Index: []"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#空值属于缺失值的一种，如下图所示该数据集无空值\n",
    "\n",
    "data[data[\"distance_from_home\"] == \"\"]\n",
    "data[data[\"distance_from_last_transaction\"] == \"\"]\n",
    "data[data[\"repeat_retailer\"] == \"\"]\n",
    "data[data[\"used_chip\"] == \"\"]\n",
    "data[data[\"used_pin_number\"] == \"\"]\n",
    "data[data[\"online_order\"] == \"\"]\n",
    "data[data[\"fraud\"] == \"\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "10cf6f46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>distance_from_home</th>\n",
       "      <th>distance_from_last_transaction</th>\n",
       "      <th>ratio_to_median_purchase_price</th>\n",
       "      <th>repeat_retailer</th>\n",
       "      <th>used_chip</th>\n",
       "      <th>used_pin_number</th>\n",
       "      <th>online_order</th>\n",
       "      <th>fraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>26.628792</td>\n",
       "      <td>5.036519</td>\n",
       "      <td>1.824182</td>\n",
       "      <td>0.881536</td>\n",
       "      <td>0.350399</td>\n",
       "      <td>0.100608</td>\n",
       "      <td>0.650552</td>\n",
       "      <td>0.087403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>65.390784</td>\n",
       "      <td>25.843093</td>\n",
       "      <td>2.799589</td>\n",
       "      <td>0.323157</td>\n",
       "      <td>0.477095</td>\n",
       "      <td>0.300809</td>\n",
       "      <td>0.476796</td>\n",
       "      <td>0.282425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.004874</td>\n",
       "      <td>0.000118</td>\n",
       "      <td>0.004399</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.878008</td>\n",
       "      <td>0.296671</td>\n",
       "      <td>0.475673</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>9.967760</td>\n",
       "      <td>0.998650</td>\n",
       "      <td>0.997717</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>25.743985</td>\n",
       "      <td>3.355748</td>\n",
       "      <td>2.096370</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10632.723672</td>\n",
       "      <td>11851.104565</td>\n",
       "      <td>267.802942</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       distance_from_home  distance_from_last_transaction  \\\n",
       "count      1000000.000000                  1000000.000000   \n",
       "mean            26.628792                        5.036519   \n",
       "std             65.390784                       25.843093   \n",
       "min              0.004874                        0.000118   \n",
       "25%              3.878008                        0.296671   \n",
       "50%              9.967760                        0.998650   \n",
       "75%             25.743985                        3.355748   \n",
       "max          10632.723672                    11851.104565   \n",
       "\n",
       "       ratio_to_median_purchase_price  repeat_retailer       used_chip  \\\n",
       "count                  1000000.000000   1000000.000000  1000000.000000   \n",
       "mean                         1.824182         0.881536        0.350399   \n",
       "std                          2.799589         0.323157        0.477095   \n",
       "min                          0.004399         0.000000        0.000000   \n",
       "25%                          0.475673         1.000000        0.000000   \n",
       "50%                          0.997717         1.000000        0.000000   \n",
       "75%                          2.096370         1.000000        1.000000   \n",
       "max                        267.802942         1.000000        1.000000   \n",
       "\n",
       "       used_pin_number    online_order           fraud  \n",
       "count   1000000.000000  1000000.000000  1000000.000000  \n",
       "mean          0.100608        0.650552        0.087403  \n",
       "std           0.300809        0.476796        0.282425  \n",
       "min           0.000000        0.000000        0.000000  \n",
       "25%           0.000000        0.000000        0.000000  \n",
       "50%           0.000000        1.000000        0.000000  \n",
       "75%           0.000000        1.000000        0.000000  \n",
       "max           1.000000        1.000000        1.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()\n",
    "#75%和max相差比较大，说明有异常值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "39d8fc57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>distance_from_home</th>\n",
       "      <th>distance_from_last_transaction</th>\n",
       "      <th>ratio_to_median_purchase_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>57.877857</td>\n",
       "      <td>0.311140</td>\n",
       "      <td>1.945940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.829943</td>\n",
       "      <td>0.175592</td>\n",
       "      <td>1.294219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.091079</td>\n",
       "      <td>0.805153</td>\n",
       "      <td>0.427715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.247564</td>\n",
       "      <td>5.600044</td>\n",
       "      <td>0.362663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>44.190936</td>\n",
       "      <td>0.566486</td>\n",
       "      <td>2.222767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999995</th>\n",
       "      <td>2.207101</td>\n",
       "      <td>0.112651</td>\n",
       "      <td>1.626798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999996</th>\n",
       "      <td>19.872726</td>\n",
       "      <td>2.683904</td>\n",
       "      <td>2.778303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999997</th>\n",
       "      <td>2.914857</td>\n",
       "      <td>1.472687</td>\n",
       "      <td>0.218075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999998</th>\n",
       "      <td>4.258729</td>\n",
       "      <td>0.242023</td>\n",
       "      <td>0.475822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999999</th>\n",
       "      <td>58.108125</td>\n",
       "      <td>0.318110</td>\n",
       "      <td>0.386920</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        distance_from_home  distance_from_last_transaction  \\\n",
       "0                57.877857                        0.311140   \n",
       "1                10.829943                        0.175592   \n",
       "2                 5.091079                        0.805153   \n",
       "3                 2.247564                        5.600044   \n",
       "4                44.190936                        0.566486   \n",
       "...                    ...                             ...   \n",
       "999995            2.207101                        0.112651   \n",
       "999996           19.872726                        2.683904   \n",
       "999997            2.914857                        1.472687   \n",
       "999998            4.258729                        0.242023   \n",
       "999999           58.108125                        0.318110   \n",
       "\n",
       "        ratio_to_median_purchase_price  \n",
       "0                             1.945940  \n",
       "1                             1.294219  \n",
       "2                             0.427715  \n",
       "3                             0.362663  \n",
       "4                             2.222767  \n",
       "...                                ...  \n",
       "999995                        1.626798  \n",
       "999996                        2.778303  \n",
       "999997                        0.218075  \n",
       "999998                        0.475822  \n",
       "999999                        0.386920  \n",
       "\n",
       "[1000000 rows x 3 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#我们主要对前三列数据进行处理，因为后几列都是0，1分布。\n",
    "data_show = data.drop(['repeat_retailer','used_chip','used_pin_number','online_order','fraud'], axis=1)\n",
    "data_show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bc10d394",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#异常值检测\n",
    "#异常值：是指数据中存在的数值明显偏离其余数据的值\n",
    "#常用的异常值检测有两种方法，一个是散点图，一个是箱线图。由于数据较多并且箱线图可以在一幅图上观察到所以特征的分布异常情况，所以我们采用箱线图观察异常值。\n",
    "\n",
    "#由下图所示，distance_from_home，distance_from_last_transaction，ratio_to_median_purchase_price中异常值较多，其余均为二分类的类别特征\n",
    "\n",
    "plt.figure(figsize=(18, 8))\n",
    "plt.boxplot(x=data_show.values,labels=data_show.columns)\n",
    "plt.hlines([-10, 10], 0, 5, colors='r')\n",
    "\n",
    "plt.show()\n",
    "#通过箱型图，我们发现数据存在异常值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8e18fbb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((700000, 7), (300000, 7), (700000,), (300000,))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#划分数据集\n",
    "from sklearn.model_selection import train_test_split\n",
    "X = data.drop('fraud', axis=1)\n",
    "y = data.fraud      #将fraud列单独摘出\n",
    "X_train, X_test,y_train, y_test = train_test_split(X, y, test_size=0.3, shuffle=True)\n",
    "X_train.shape, X_test.shape,y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3ba255ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# #异常值处理\n",
    "# #基于模型预测检测异常值的函数\n",
    "# def find_outliers(model, X, y, sigma=3): #删除大于3σ的异常值\n",
    "\n",
    "#     # 用模型预测y值\n",
    "#     try:\n",
    "#         y_pred = pd.Series(model.predict(X), index=y.index)\n",
    "#     # 如果预测失败，请先尝试拟合模型\n",
    "#     except:\n",
    "#         model.fit(X,y)\n",
    "#         y_pred = pd.Series(model.predict(X), index=y.index)\n",
    "        \n",
    "#     # 计算模型预测值和真实y值之间的残差\n",
    "#     resid = y - y_pred #残差\n",
    "#     mean_resid = resid.mean() #残差平均值\n",
    "#     std_resid = resid.std() #残差的方差\n",
    "\n",
    "#     # 计算z统计，将异常值定义为|z|>sigma\n",
    "#     z = (resid - mean_resid)/std_resid    #残差与均值之差超过3个方差则视为异常值\n",
    "#     outliers = z[abs(z)>sigma].index\n",
    "    \n",
    "#     # 打印并画出结果\n",
    "#     print('R2=',model.score(X,y))\n",
    "#     print(\"mse=\",mean_squared_error(y,y_pred)) #均方差\n",
    "#     print('---------------------------------------')\n",
    "\n",
    "#     print('残差平均值:',mean_resid)\n",
    "#     print('残差的方差:',std_resid)\n",
    "#     print('---------------------------------------')\n",
    "\n",
    "#     print(len(outliers),'outliers:')\n",
    "#     print(outliers.tolist()) #将数组转化为列表，输出异常值所在行\n",
    "\n",
    "#     return outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5c5dbf43",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.linear_model import LogisticRegression\n",
    "# from sklearn.metrics import mean_squared_error\n",
    "# # X_train_copy1 = X_train.copy()#用于处理异常值的另一种方法\n",
    "\n",
    "# #用数据预测y y值与均值之差 超过多少（sigma）个方差的 视为异常值\n",
    "# outliers= find_outliers(LogisticRegression(), X_train, y_train, sigma=3)\n",
    "# # 删除异常值\n",
    "# X_train=X_train.drop(outliers)\n",
    "# y_train=y_train.drop(outliers)\n",
    "# X_train.shape, y_train.shape\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "00fd27d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#异常值处理\n",
    "#用中位数替代偏离很大的值。\n",
    "from scipy.stats import zscore\n",
    "from numpy import where,abs,median,nan,sqrt\n",
    "def ChuliYC(data, columns):\n",
    "    #计算z-score值，大于3倍方差的视为异常值，用中位数替代\n",
    "    for i in columns: \n",
    "        data[i] = data[i].replace([data[i][(abs(zscore(data[i])) > 3)]], median(data[i]))\n",
    "    return data\n",
    "X_train = ChuliYC(X_train, X_train.columns) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e36187e1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "07f327da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 700000 entries, 130248 to 344405\n",
      "Data columns (total 7 columns):\n",
      " #   Column                          Non-Null Count   Dtype  \n",
      "---  ------                          --------------   -----  \n",
      " 0   distance_from_home              700000 non-null  float64\n",
      " 1   distance_from_last_transaction  700000 non-null  float64\n",
      " 2   ratio_to_median_purchase_price  700000 non-null  float64\n",
      " 3   repeat_retailer                 700000 non-null  float64\n",
      " 4   used_chip                       700000 non-null  float64\n",
      " 5   used_pin_number                 700000 non-null  float64\n",
      " 6   online_order                    700000 non-null  float64\n",
      "dtypes: float64(7)\n",
      "memory usage: 42.7 MB\n"
     ]
    }
   ],
   "source": [
    "X_train.info()\n",
    "#由于数据不存在缺失值，所以不需要进行缺失值处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9fa6f5dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#多重共线性的分析\n",
    "## 相关关系矩阵热力图\n",
    "import seaborn as sns\n",
    "sns.heatmap(data.corr(), annot=True, cmap='RdBu', xticklabels=1, yticklabels=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6dc2e842",
   "metadata": {},
   "outputs": [],
   "source": [
    "#归一化，去除量纲的影响。分布到0-1之间\n",
    "from sklearn import preprocessing\n",
    "\n",
    "features_columns = list(X_train.columns)\n",
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "min_max_scaler = min_max_scaler.fit(X_train[features_columns]) #根据训练集拟合归一化，max和min都是训练集的。\n",
    "\n",
    "#对训练集和测试集归一化\n",
    "X_train_scaler = pd.DataFrame(min_max_scaler.transform(X_train[features_columns]))\n",
    "X_test_scaler = pd.DataFrame(min_max_scaler.transform(X_test[features_columns]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8f0e0843",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>700000.000000</td>\n",
       "      <td>700000.000000</td>\n",
       "      <td>700000.000000</td>\n",
       "      <td>700000.000000</td>\n",
       "      <td>700000.000000</td>\n",
       "      <td>700000.000000</td>\n",
       "      <td>700000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.094593</td>\n",
       "      <td>0.044071</td>\n",
       "      <td>0.152272</td>\n",
       "      <td>0.881261</td>\n",
       "      <td>0.350546</td>\n",
       "      <td>0.100024</td>\n",
       "      <td>0.650431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.138366</td>\n",
       "      <td>0.096995</td>\n",
       "      <td>0.163441</td>\n",
       "      <td>0.323481</td>\n",
       "      <td>0.477141</td>\n",
       "      <td>0.300033</td>\n",
       "      <td>0.476834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.017153</td>\n",
       "      <td>0.003475</td>\n",
       "      <td>0.046095</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.044229</td>\n",
       "      <td>0.011709</td>\n",
       "      <td>0.097172</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.107899</td>\n",
       "      <td>0.037821</td>\n",
       "      <td>0.193035</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   0              1              2              3  \\\n",
       "count  700000.000000  700000.000000  700000.000000  700000.000000   \n",
       "mean        0.094593       0.044071       0.152272       0.881261   \n",
       "std         0.138366       0.096995       0.163441       0.323481   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.017153       0.003475       0.046095       1.000000   \n",
       "50%         0.044229       0.011709       0.097172       1.000000   \n",
       "75%         0.107899       0.037821       0.193035       1.000000   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "                   4              5              6  \n",
       "count  700000.000000  700000.000000  700000.000000  \n",
       "mean        0.350546       0.100024       0.650431  \n",
       "std         0.477141       0.300033       0.476834  \n",
       "min         0.000000       0.000000       0.000000  \n",
       "25%         0.000000       0.000000       0.000000  \n",
       "50%         0.000000       0.000000       1.000000  \n",
       "75%         1.000000       0.000000       1.000000  \n",
       "max         1.000000       1.000000       1.000000  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_scaler.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "912b4a64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>300000.000000</td>\n",
       "      <td>300000.000000</td>\n",
       "      <td>300000.000000</td>\n",
       "      <td>300000.000000</td>\n",
       "      <td>300000.000000</td>\n",
       "      <td>300000.000000</td>\n",
       "      <td>300000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.118497</td>\n",
       "      <td>0.059681</td>\n",
       "      <td>0.178049</td>\n",
       "      <td>0.882177</td>\n",
       "      <td>0.350057</td>\n",
       "      <td>0.101970</td>\n",
       "      <td>0.650833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.283078</td>\n",
       "      <td>0.276906</td>\n",
       "      <td>0.274178</td>\n",
       "      <td>0.322399</td>\n",
       "      <td>0.476988</td>\n",
       "      <td>0.302609</td>\n",
       "      <td>0.476708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.000073</td>\n",
       "      <td>-0.000002</td>\n",
       "      <td>0.000169</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.017133</td>\n",
       "      <td>0.003480</td>\n",
       "      <td>0.046196</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.044209</td>\n",
       "      <td>0.011721</td>\n",
       "      <td>0.097327</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.114462</td>\n",
       "      <td>0.039461</td>\n",
       "      <td>0.204915</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>21.445585</td>\n",
       "      <td>40.325053</td>\n",
       "      <td>26.102746</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   0              1              2              3  \\\n",
       "count  300000.000000  300000.000000  300000.000000  300000.000000   \n",
       "mean        0.118497       0.059681       0.178049       0.882177   \n",
       "std         0.283078       0.276906       0.274178       0.322399   \n",
       "min        -0.000073      -0.000002       0.000169       0.000000   \n",
       "25%         0.017133       0.003480       0.046196       1.000000   \n",
       "50%         0.044209       0.011721       0.097327       1.000000   \n",
       "75%         0.114462       0.039461       0.204915       1.000000   \n",
       "max        21.445585      40.325053      26.102746       1.000000   \n",
       "\n",
       "                   4              5              6  \n",
       "count  300000.000000  300000.000000  300000.000000  \n",
       "mean        0.350057       0.101970       0.650833  \n",
       "std         0.476988       0.302609       0.476708  \n",
       "min         0.000000       0.000000       0.000000  \n",
       "25%         0.000000       0.000000       0.000000  \n",
       "50%         0.000000       0.000000       1.000000  \n",
       "75%         1.000000       0.000000       1.000000  \n",
       "max         1.000000       1.000000       1.000000  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_scaler.describe()\n",
    "#max和min不是1和0，因为是根据训练集fit模型的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4f22b604",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1728x864 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#画出直方图和QQ图查看数据的分布\n",
    "train_cols = 6 #6列\n",
    "train_rows = len(data_show.columns)\n",
    "plt.figure(figsize=(4*train_cols,4*train_rows)) #设置画布大小\n",
    "\n",
    "i=0\n",
    "for col in data_show.columns:\n",
    "    #属性直方图\n",
    "    i+=1\n",
    "    ax=plt.subplot(train_rows,train_cols,i)\n",
    "    sns.distplot(data_show[col],fit=stats.norm) #fit设置函数正态分布的图像与原图比较\n",
    "    \n",
    "    #属性的QQ图，检验某属性是否符合正态分布\n",
    "    i+=1\n",
    "    ax=plt.subplot(train_rows,train_cols,i)\n",
    "    res = stats.probplot(data_show[col], plot=plt)\n",
    "plt.tight_layout()\n",
    "filename = \"./标准化前的直方图和qq图.png\"\n",
    "plt.savefig(filename) #导出该图像。\n",
    "plt.show()\n",
    "#对于后四个数据01分布的数据不进行标准化操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "cb990aba",
   "metadata": {},
   "outputs": [],
   "source": [
    "#标准化\n",
    "# RobustScaler准化,如果要最大限度保留数据集中的异常，则使用RobustScaler方法\n",
    "#https://www.cnblogs.com/keye/p/14964266.html\n",
    "\n",
    "#使用sklearn实现 Yeo-Johnson来标准化\n",
    "from sklearn.preprocessing import PowerTransformer \n",
    "pt =PowerTransformer()    #这里method 默认是Yeo-johnson\n",
    "pt.fit(X_train_scaler)\n",
    "\n",
    "#对训练集和数据集标准化\n",
    "X_train_s_bc=pt.transform(X_train_scaler)\n",
    "\n",
    "X_test_s_bc = pt.transform(X_test_scaler)\n",
    "\n",
    "X_train_s_bc = pd.DataFrame(X_train_s_bc)\n",
    "X_test_s_bc = pd.DataFrame(X_test_s_bc)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "eea3f119",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x864 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#画图，标准化后的直方图和QQ图\n",
    "train_cols=6\n",
    "train_rows =len(X_train_s_bc.columns[:3])\n",
    "plt.figure(figsize=(4*train_cols,4*train_rows))\n",
    "i=0\n",
    "for col in X_train_s_bc.columns[:3]:\n",
    "    i+=1\n",
    "    ax=plt.subplot(train_rows,train_cols,i)\n",
    "    sns.distplot(X_train_s_bc[col],fit=stats.norm)\n",
    "    i+=1\n",
    "    ax=plt.subplot(train_rows,train_cols,i) \n",
    "    res = stats.probplot(X_train_s_bc[col], plot=plt) \n",
    "plt.tight_layout()\n",
    "plt.show\n",
    "filename = \"./标准化后的直方图和qq图.png\"\n",
    "plt.savefig(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f856e554",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "# from sklearn.metrics import roc_auc_score, classification_report, roc_curve, auc, plot_confusion_matrix, precision_score, recall_score, f1_score \n",
    "\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "from imblearn.pipeline import Pipeline\n",
    "from joblib import dump, load\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a9aa25b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "过采样前，1的样本的个数为： 61150\n",
      "过采样前，0的样本的个数为： 638850\n",
      "过采样后，1的样本的个数为： 638850\n",
      "过采样后，0的样本的个数为： 638850\n"
     ]
    }
   ],
   "source": [
    "# 利用SMOTE进行过采样\n",
    "\n",
    "print('过采样前，1的样本的个数为：',len(y_train[y_train==1]))\n",
    "print('过采样前，0的样本的个数为：',len(y_train[y_train==0]))\n",
    "over_sampler=SMOTE(random_state=0)\n",
    "X_os_train,y_os_train=over_sampler.fit_resample(X_train_s_bc,y_train)\n",
    "print('过采样后，1的样本的个数为：',len(y_os_train[y_os_train==1]))\n",
    "print('过采样后，0的样本的个数为：',len(y_os_train[y_os_train==0]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e2a2ef39",
   "metadata": {},
   "outputs": [],
   "source": [
    "#模型训练\n",
    "#导入工具包\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from scipy import stats\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "## 导入工具\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "import warnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e1253dfa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "调参前准确率: 0.9583833333333334\n"
     ]
    }
   ],
   "source": [
    "#调参前的逻辑回归模型\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import cross_val_score\n",
    "model = LogisticRegression()\n",
    "model.fit(X_train,y_train)\n",
    "y_pred_1 = model.predict(X_test)\n",
    "from sklearn.metrics import accuracy_score\n",
    "# 计算准确度\n",
    "print('调参前准确率:',accuracy_score(y_pred_1,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3c9d0956",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "调参后准确率： 0.95998\n"
     ]
    }
   ],
   "source": [
    "#模型训练：逻辑回归\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "penaltys = ['l1','l2'] #惩罚项，如果需要正则化，L1,L2两个参数\n",
    "Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000] #参数C：正则化强度的倒数\n",
    "tuned_parameters = dict(penalty = penaltys, C = Cs) #转换为参数字典\n",
    "lr_penalty= LogisticRegression() #逻辑回归模型\n",
    "grid= GridSearchCV(lr_penalty, tuned_parameters,cv=5, scoring='neg_log_loss')#cv=5交叉验证5折，scoing评分标准\n",
    "grid.fit(X_train,y_train)\n",
    "\n",
    "#使用找到的最好参数\n",
    "lr=LogisticRegression(C=grid.best_params_[\"C\"],penalty = grid.best_params_[\"penalty\"]) #找到最好的参数\n",
    "lr.fit(X_train,y_train)\n",
    "y_pred = lr.predict(X_test)\n",
    "from sklearn.metrics import accuracy_score\n",
    "# 计算准确度\n",
    "print('调参后准确率：',accuracy_score( y_pred,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "115251f2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "f3ac33be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.92666"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# #模型朴素贝叶斯\n",
    "# from sklearn.naive_bayes import GaussianNB\n",
    "# GaussianNB = GaussianNB()\n",
    "# GaussianNB.fit(X_train,y_train)\n",
    "# y_pred = GaussianNB.predict(X_test)\n",
    "# from sklearn.metrics import accuracy_score\n",
    "# # 计算准确度\n",
    "# accuracy_score(y_pred,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77bdfba7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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